Five Tribes of Machine Learning Applications
Here's another nice shortcut to understanding what types of machine learning are getting used in business today.
IT professionals and experts are talking about a book called “The Master Algorithm” by Pedro Domingos, where this groundbreaking academic talks about five groups of machine learning scientists.
Domingos talks about “symbolists” who use inverse deduction and work from a set of known data, often using neural networks sort of like logic gates as Minsky used to talk about. Symbolists might apply a lot of supervised machine learning processes to large sets of annotated training data, which is one of the more labor-intensive ways of doing machine learning. However, it's easily analyzed and allows humans and machines to work together for a highly collaborative result.
The second school is the connectionist – these are scientists who are very focused on simulating the neuroscience of the brain. Connectionists are likely to keep looking at how to streamline neural network design according to models in biophysics. They may apply some semi-supervised learning structures to try to boost the power of neural networks and make them more like the activity of the human brain – a good example would be some of the third-generation tools discussed above, where adding a chronological element builds the reality of neural networks.
A third tribe called evolutionaries is focused on machine learning and genetics – crunching the data on DNA, unraveling the human genome and applying machine learning to health science in particular ways. Genetic neural networks are getting applied in many ways – looking at gene editing, sophisticated DNA work and sometimes a “Darwinian” approach to machine selection.
A fourth school of machine learning goes back to some more traditional technologies and resources. The Bayesian school uses probability theory and heuristic models to build machine learning results. One example that experts most often give is the set of tools for email spam filtering. Email spam filtering existed before neural networks and machine learning took off – but the ability to run information through neural networks is supercharging the Bayesian logic that we use to do all sorts of things in today's business world.
The fifth school is the analogizers – the idea that you can match bits of data together for machine learning outcomes. These scientists are heavy on using muscular algorithms to work on data sets. They use things like nearest neighbor algorithms and random walk algorithms, and they like to build tools like recommendation engines which have been some of the most popular uses of machine learning.